Spectral domain optical coherence tomography (SD-OCT) is a new high resolution imaging technique, capable of achieving micrometer resolution in depth. It allows detailed imaging of the eye structures. Three-dimensional (3D) OCT, which can yield 3D (cube) images of the retina, is a promising technique for automated analysis, early detection, and monitoring the progression of eye diseases, such as glaucoma, diabetic retinopathy and others.
A blood vessel on a retinal image is not only an indicator of various eye diseases, but also an important feature to register the retinal images of the same patient taken at different visits, or even taken with different ophthalmic devices. This greatly improves accuracy in the monitoring of eye disease progression. Additionally, a blood vessel can be used as a landmark to measure other normal or abnormal features on the retina.
A 3D OCT retinal image is comprised of a series of cross-sectional scans (B-scan, x-z plane in FIG. 1(a)) from top to bottom of the scanning region of the retina. Each B-scan consists of certain number of high-resolution one-dimensional scan in z direction (A-scan). A blood vessel generates a shadow on the outer retinal layers, as shown in FIG. 1(b). When an OCT fundus image is generated (FIG. 1(c)) by averaging each A-scan, the visibility of vessel pattern may decrease due to the noise and high reflection on the top layers of the retina. This makes vessel segmentation on OCT image a challenge, compared to a conventional 2D fundus image.
Niemeijer et al. [1] introduced an automated vessel segmentation algorithm for 3D OCT imaging. Retinal layer segmentation was required to generate a 2D OCT fundus image by taking the 2D projection of certain retinal layers, thereby to enhance vessel pattern. A supervised, pixel-classification approach was applied to 2D projection to detect blood vessel. The authors tested the approach on the samples with well-segmented retinal layers and obtained 0.97 of an area under the ROC curve. This approach is limited in that its performance highly depends on retinal layer segmentation.
Most other retinal vessel segmentation techniques are based on conventional 2D fundus imaging, which can be roughly classified into several major categories: matched filter based method [2,3], thresholding based method [4], tracking based method [5], mathematical morphology method [6], and classification based method [7,8]. The matched filter method entails convolving the image with a directional filter designed according to the vessel profile [3], such as Gaussian [2] and second-order Gaussian filter [3]. Hoover et al. [4] introduced a piecewise thresholding probe algorithm, for matched filter response (MFR) imaging, which yielded a 90% true positive detection rate compared with the hand-labeled blood vessel as the ground truth. Staal et al. [7] described a pixel classification method for color retinal imaging. Feature vectors first were computed for each pixel, and then a k-Nearest neighbor (kNN) classifier was used to classify a given pixel as either vessel or non-vessel.